Abstract
<p>It is known that humans leverage regularities in form-meaning mapping to answer the semasiological question “what does X mean?”. Yet, in everyday communication we also need the opposite, that is answering the onomasiological question “how do I express X?”. In this study, we explored whether regularities between form and meaning estimated through Linear Discriminative Learning can be used to create novel words that convey a desired meaning. We conducted three online experiments to explore whether human participants were able to identify a generated pseudoword resulting from one single word prompt (i.e., a “pseudo-synonym”, Experiment 1), a pair of word prompts whose combined meaning produced a certain pseudoword (Experiment 2), and a generated pseudoword resulting from the combined meaning of a pair of word prompts (Experiment 3). Across all three experiments, participants proved to be able to identify the correct pseudoword(s)-prompt(s) association, and semantic similarity between response options proved to explain human performance over and above surface similarity. Furthermore, we conducted two additional experiments (Experiments 4 and 5) in which we highlighted that computational predictions based on meaning-form mapping align with human novel-word production in a taboo-game-paradigm task. These results suggest that regularities between form and meaning not only support language comprehension but also play a role in the formulation of novel ideas in speech production. Mappings from a semantic space to a form space provide a new tool for simulating the production of (and actually generating) novel terms.</p>